Prevalence of Short Peer Reviews in 3 Leading General Medical Journals.

Importance: High-quality peer reviews are often thought to be essential to ensuring the integrity of the scientific publication process, but measuring peer review quality is challenging. Although imperfect, review word count could potentially serve as a simple, objective metric of review quality. Objective: To determine the prevalence of very short peer reviews and how often they inform editorial decisions on research articles in 3 leading general medical journals. Design, Setting, and Participants: This cross-sectional study compiled a data set of peer reviews from published, full-length original research articles from 3 general medical journals (The BMJ, PLOS Medicine, and BMC Medicine) between 2003 and 2022. Eligible articles were those with peer review data; all peer reviews used to make the first editorial decision (ie, accept vs revise and resubmit) were included. Main Outcomes and Measures: Prevalence of very short reviews was the primary outcome, which was defined as a review of fewer than 200 words. In secondary analyses, thresholds of fewer than 100 words and fewer than 300 words were used. Results were disaggregated by journal and year. The proportion of articles for which the first editorial decision was made based on a set of peer reviews in which very short reviews constituted 100%, 50% or more, 33% or more, and 20% or more of the reviews was calculated. Results: In this sample of 11 466 reviews (including 6086 in BMC Medicine, 3816 in The BMJ, and 1564 in PLOS Medicine) corresponding to 4038 published articles, the median (IQR) word count per review was 425 (253-575) words, and the mean (SD) word count was 520.0 (401.0) words. The overall prevalence of very short (<200 words) peer reviews was 1958 of 11 466 reviews (17.1%). Across the 3 journals, 843 of 4038 initial editorial decisions (20.9%) were based on review sets containing 50% or more very short reviews. The prevalence of very short reviews and share of editorial decisions based on review sets containing 50% or more very short reviews was highest for BMC Medicine (693 of 2585 editorial decisions [26.8%]) and lowest for The BMJ (76 of 1040 editorial decisions [7.3%]). Conclusion and Relevance: In this study of 3 leading general medical journals, one-fifth of initial editorial decisions for published articles were likely based at least partially on reviews of such short length that they were unlikely to be of high quality. Future research could determine whether monitoring peer review length improves the quality of peer reviews and which interventions, such as incentives and norm-based interventions, may elicit more detailed reviews.

Healthcare worker attendance during the early stages of the COVID-19 pandemic: A longitudinal analysis of fingerprint-verified data from all public-sector secondary and tertiary care facilities in Bangladesh.

BACKGROUND: The COVID-19 pandemic has overwhelmed hospitals in several areas in high-income countries. An effective response to this pandemic requires health care workers (HCWs) to be present at work, particularly in low- and middle-income countries (LMICs) where they are already in critically low supply. To inform whether and to what degree policymakers in Bangladesh, and LMICs more broadly, should expect a drop in HCW attendance as COVID-19 continues to spread, this study aims to determine how HCW attendance has changed during the early stages of the COVID-19 pandemic in Bangladesh. METHODS: This study analyzed daily fingerprint-verified attendance data from all 527 public-sector secondary and tertiary care facilities in Bangladesh to describe HCW attendance from January 26, 2019 to March 22, 2020, by cadre, hospital type, and geographic division. We then regressed HCW attendance onto fixed effects for day-of-week, month, and hospital, as well as indicators for each of three pandemic periods: a China-focused period (January 11, 2020 (first confirmed COVID-19 death in China) until January 29, 2020), international-spread period (January 30, 2020 (World Health Organization's declaration of a global emergency) until March 6, 2020), and local-spread period (March 7, 2020 (first confirmed COVID-19 case in Bangladesh) until the end of the study period). FINDINGS: On average between January 26, 2019 and March 22, 2020, 34.1% of doctors, 64.6% of nurses, and 70.6% of other health care staff were present for their scheduled shift. HCWs' attendance rate increased with time in 2019 among all cadres. Nurses' attendance level dropped by 2.5% points (95% confidence interval (CI) = -3.2% to -1.8%) and 3.5% points (95% CI = -4.5% to -2.5%) during the international-spread and the local-spread periods of the COVID-19 pandemic, relative to the China-focused period. Similarly, the attendance level of other health care staff declined by 0.3% points (95% CI = -0.8% to 0.2%) and 2.3% points (95% CI = -3.0% to -1.6%) during the international-spread and local-spread periods, respectively. Among doctors, however, the international-spread and local-spread periods were associated with a statistically significant increase in attendance by 3.7% points (95% CI = 2.5% to 4.8%) and 4.9% points (95% CI = 3.5% to 6.4%), respectively. The reduction in attendance levels across all HCWs during the local-spread period was much greater at large hospitals, where the majority of COVID-19 testing and treatment took place, than that at small hospitals. CONCLUSIONS: After a year of significant improvements, HCWs' attendance levels among nurses and other health care staff (who form the majority of Bangladesh's health care workforce) have declined during the early stages of the COVID-19 pandemic. This finding may portend an even greater decrease in attendance if COVID-19 continues to spread in Bangladesh. Policymakers in Bangladesh and similar LMICs should undertake major efforts to achieve high attendance levels among HCWs, particularly nurses, such as by providing sufficient personal protective equipment as well as monetary and non-monetary incentives.

Healthcare Worker Attendance During the Early Stages of the COVID-19 Pandemic: A Longitudinal Analysis of Daily Fingerprint-Verified Data from All Public-Sector Secondary and Tertiary Care Facilities in Bangladesh.

BACKGROUND: The COVID-19 pandemic has overwhelmed hospitals in several areas in high-income countries. An effective response to this pandemic requires healthcare workers (HCWs) to be present at work, particularly in low- and middle-income countries (LMICs) where they are already in critically low supply. To inform whether and to what degree policymakers in Bangladesh, and LMICs more broadly, should expect a drop in HCW attendance as COVID-19 continues to spread, this study aims to determine how HCW attendance has changed during the early stages of the COVID-19 pandemic in Bangladesh. METHODS: This study analyzed daily fingerprint-verified attendance data from all 527 public-sector secondary and tertiary care facilities in Bangladesh to describe HCW attendance from January 26, 2019 to March 22, 2020, by cadre, hospital type, and geographic division. We then regressed HCW attendance onto fixed effects for day-of-week, month, and hospital, as well as indicators for each of three pandemic periods: a China-focused period (January 11, 2020 [first confirmed COVID-19 death in China] until January 29, 2020), international-spread period (January 30, 2020 [World Health Organization declared a global emergency] until March 6, 2020), and local-spread period (March 7, 2020 [first confirmed COVID-19 case in Bangladesh] until the end of the study period). FINDINGS: On average between January 26, 2019 and March 22, 2020, 34.1% of doctors, 64.6% of nurses, and 70.6% of other healthcare staff were present for their scheduled shift. Attendance rate increased with time in 2019 among all cadres. Attendance level of nurses dropped by 2.5% points (95% CI; -3.2% to -1.8%) and 3.5% points (95% CI; -4.5% to -2.5%) during the international-spread and the local-spread periods of the COVID-19 pandemic, relative to the China-focused period. Similarly, the attendance level of other healthcare staff declined by 0.3% points (95% CI; -0.8% to 0.2%) and 2.3% points (95% CI; -3.0% to -1.6%) during the international-spread and local-spread periods, respectively. Among doctors, however, the international-spread and local-spread periods were associated with a statistically significant increase in attendance by 3.7% points (95% CI; 2.5% to 4.8%) and 4.9% points (95% CI; 3.5% to 6.4%), respectively. The reduction in attendance levels across all HCWs during the local-spread period was much greater at large hospitals, where the majority of COVID-19 testing and treatment took place, than that at small hospitals. CONCLUSIONS: After a year of significant improvements, attendance levels among nurses and other healthcare staff (who form the majority of the healthcare workforce in Bangladesh) have declined during the early stages of the COVID-19 pandemic. This finding may portend an even greater decrease in attendance if COVID-19 continues to spread in Bangladesh. Policymakers in Bangladesh and similar LMICs should undertake major efforts to achieve high attendance levels among HCWs, particularly nurses, such as by providing sufficient personal protective equipment as well as monetary and non-monetary incentives.

Automated Indoor Image Localization to Support a Post-Event Building Assessment.

Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. The inability to document the images' locations hinders the analysis, organization, and documentation of these images as they lack sufficient spatial context. In this work, we develop a methodology to localize images and link them to locations on a structural drawing. A stream of images can readily be gathered along the path taken through a building using a compact camera. These images may be used to compute a relative location of each image in a 3D point cloud model, which is reconstructed using a visual odometry algorithm. The images may also be used to create local 3D textured models for building-components-of-interest using a structure-from-motion algorithm. A parallel set of images that are collected for building assessment is linked to the image stream using time information. By projecting the point cloud model to the structural drawing, the images can be overlaid onto the drawing, providing clear context information necessary to make use of those images. Additionally, components- or damage-of-interest captured in these images can be reconstructed in 3D, enabling detailed assessments having sufficient geospatial context. The technique is demonstrated by emulating post-event building assessment and data collection in a real building.